PaddleX Model List (Enflame GCU)¶
PaddleX incorporates multiple pipelines, each containing several modules, and each module encompasses various models. You can select the appropriate models based on the benchmark data below. If you prioritize model accuracy, choose models with higher accuracy. If you prioritize model size, select models with smaller storage requirements.
Image Classification Module¶
| Model Name | Top-1 Accuracy (%) | Model Size (M) | Model Download Link |
|---|---|---|---|
| ConvNeXt_base_224 | 83.84 | 313.9 M | Inference Model/Trained Model |
| ConvNeXt_base_384 | 84.90 | 313.9 M | Inference Model/Trained Model |
| ConvNeXt_large_224 | 84.26 | 700.7 M | Inference Model/Trained Model |
| ConvNeXt_large_384 | 85.27 | 700.7 M | Inference Model/Trained Model |
| ConvNeXt_small | 83.13 | 178.0 M | Inference Model/Trained Model |
| ConvNeXt_tiny | 82.03 | 101.4 M | Inference Model/Trained Model |
| FasterNet-L | 83.5 | 357.1 M | Inference Model/Trained Model |
| FasterNet-M | 82.9 | 204.6 M | Inference Model/Trained Model |
| FasterNet-S | 81.3 | 119.3 M | Inference Model/Trained Model |
| FasterNet-T0 | 71.8 | 15.1 M | Inference Model/Trained Model |
| FasterNet-T1 | 76.2 | 29.2 M | Inference Model/Trained Model |
| FasterNet-T2 | 78.8 | 57.4 M | Inference Model/Trained Model |
| MobileNetV1_x0_25 | 51.4 | 1.8 M | Inference Model/Trained Model |
| MobileNetV1_x0_5 | 63.5 | 4.8 M | Inference Model/Trained Model |
| MobileNetV1_x0_75 | 68.8 | 9.3 M | Inference Model/Trained Model |
| MobileNetV1_x1_0 | 71.0 | 15.2 M | Inference Model/Trained Model |
| MobileNetV2_x0_25 | 53.2 | 5.5 M | Inference Model/Trained Model |
| MobileNetV2_x0_5 | 65.0 | 7.1 M | Inference Model/Trained Model |
| MobileNetV2_x1_0 | 72.2 | 12.6 M | Inference Model/Trained Model |
| MobileNetV2_x1_5 | 74.1 | 25.0 M | Inference Model/Trained Model |
| MobileNetV2_x2_0 | 75.2 | 41.2 M | Inference Model/Trained Model |
| MobileNetV3_large_x0_35 | 64.3 | 7.5 M | Inference Model/Trained Model |
| MobileNetV3_large_x0_5 | 69.2 | 9.6 M | Inference Model/Trained Model |
| MobileNetV3_large_x0_75 | 73.1 | 14.0 M | Inference Model/Trained Model |
| MobileNetV3_large_x1_0 | 75.3 | 19.5 M | Inference Model/Trained Model |
| MobileNetV3_large_x1_25 | 76.4 | 26.5 M | Inference Model/Trained Model |
| MobileNetV3_small_x0_35 | 53.0 | 6.0 M | Inference Model/Trained Model |
| MobileNetV3_small_x0_5 | 59.2 | 6.8 M | Inference Model/Trained Model |
| MobileNetV3_small_x0_75 | 66.0 | 8.5 M | Inference Model/Trained Model |
| MobileNetV3_small_x1_0 | 68.2 | 10.5 M | Inference Model/Trained Model |
| MobileNetV3_small_x1_25 | 70.7 | 13.0 M | Inference Model/Trained Model |
| MobileNetV4_conv_large | 83.4 | 125.2 M | Inference Model/Trained Model |
| MobileNetV4_conv_medium | 80.9 | 37.6 M | Inference Model/Trained Model |
| MobileNetV4_conv_small | 74.4 | 14.7 M | Inference Model/Trained Model |
| PP-HGNet_base | 85.0 | 249.4 M | Inference Model/Trained Model |
| PP-HGNet_small | 81.51 | 86.5 M | Inference Model/Trained Model |
| PP-HGNet_tiny | 79.83 | 52.4 M | Inference Model/Trained Model |
| PP-HGNetV2-B0 | 77.77 | 21.4 M | Inference Model/Trained Model |
| PP-HGNetV2-B1 | 78.90 | 22.6 M | Inference Model/Trained Model |
| PP-HGNetV2-B2 | 81.57 | 39.9 M | Inference Model/Trained Model |
| PP-HGNetV2-B3 | 82.92 | 57.9 M | Inference Model/Trained Model |
| PP-HGNetV2-B4 | 83.68 | 70.4 M | Inference Model/Trained Model |
| PP-HGNetV2-B5 | 84.75 | 140.8 M | Inference Model/Trained Model |
| PP-HGNetV2-B6 | 86.20 | 268.4 M | Inference Model/Trained Model |
| PP-LCNet_x0_25 | 51.86 | 5.5 M | Inference Model/Trained Model |
| PP-LCNet_x0_35 | 58.10 | 5.9 M | Inference Model/Trained Model |
| PP-LCNet_x0_5 | 63.14 | 6.7 M | Inference Model/Trained Model |
| PP-LCNet_x0_75 | 68.18 | 8.4 M | Inference Model/Trained Model |
| PP-LCNet_x1_0 | 71.32 | 10.5 M | Inference Model/Trained Model |
| PP-LCNet_x1_5 | 73.71 | 16.0 M | Inference Model/Trained Model |
| PP-LCNet_x2_0 | 75.18 | 23.2 M | Inference Model/Trained Model |
| PP-LCNet_x2_5 | 76.60 | 32.1 M | Inference Model/Trained Model |
| PP-LCNetV2_base | 77.04 | 23.7 M | Inference Model/Trained Model |
| PP-LCNetV2_large | 78.51 | 37.3 M | Inference Model/Trained Model |
| PP-LCNetV2_small | 73.96 | 14.6 M | Inference Model/Trained Model |
| ResNet18_vd | 72.3 | 41.5 M | Inference Model/Trained Model |
| ResNet18 | 71.0 | 41.5 M | Inference Model/Trained Model |
| ResNet34_vd | 76.0 | 77.3 M | Inference Model/Trained Model |
| ResNet34 | 74.6 | 77.3 M | Inference Model/Trained Model |
| ResNet50_vd | 79.1 | 90.8 M | Inference Model/Trained Model |
| ResNet50 | 76.5 | 90.8 M | Inference Model/Trained Model |
| ResNet101_vd | 80.2 | 158.4 M | Inference Model/Trained Model |
| ResNet101 | 77.6 | 158.7 M | Inference Model/Trained Model |
| ResNet152_vd | 80.6 | 214.3 M | Inference Model/Trained Model |
| ResNet152 | 78.3 | 214.2 M | Inference Model/Trained Model |
| ResNet200_vd | 80.7 | 266.0 M | Inference Model/Trained Model |
| StarNet-S1 | 73.5 | 11.2 M | Inference Model/Trained Model |
| StarNet-S2 | 74.7 | 14.3 M | Inference Model/Trained Model |
| StarNet-S3 | 77.4 | 22.2 M | Inference Model/Trained Model |
| StarNet-S4 | 78.8 | 28.9 M | Inference Model/Trained Model |
Note: The above accuracy metrics refer to Top-1 Accuracy on the ImageNet-1k validation set.
Object Detection Module¶
| Model Name | mAP (%) | Model Size (M) | Model Download Link |
|---|---|---|---|
| FCOS-ResNet50 | 39.6 | 124.2 M | Inference Model/Trained Model |
| PicoDet-L | 42.5 | 20.9 M | Inference Model/Trained Model |
| PicoDet-M | 37.4 | 16.8 M | Inference Model/Trained Model |
| PicoDet-S | 29.0 | 4.4 M | Inference Model/Trained Model |
| PicoDet-XS | 26.2 | 5.7M | Inference Model/Trained Model |
| PP-YOLOE_plus-L | 52.8 | 185.3 M | Inference Model/Trained Model |
| PP-YOLOE_plus-M | 49.7 | 83.2 M | Inference Model/Trained Model |
| PP-YOLOE_plus-S | 43.6 | 28.3 M | Inference Model/Trained Model |
| PP-YOLOE_plus-X | 54.7 | 349.4 M | Inference Model/Trained Model |
| RT-DETR-H | 56.3 | 435.8 M | Inference Model/Trained Model |
| RT-DETR-L | 53.0 | 113.7 M | Inference Model/Trained Model |
| RT-DETR-R18 | 46.5 | 70.7 M | Inference Model/Trained Model |
| RT-DETR-R50 | 53.1 | 149.1 M | Inference Model/Trained Model |
| RT-DETR-X | 54.8 | 232.9 M | Inference Model/Trained Model |
Note: The above accuracy metrics are for COCO2017 validation set mAP(0.5:0.95).
Pedestrian Detection Module¶
| Model Name | mAP（%） | Model Size (M) | Model Download Link |
|---|---|---|---|
| PP-YOLOE-L_human | 48.0 | 196.1 M | Inference Model/Trained Model |
| PP-YOLOE-S_human | 42.5 | 28.8 M | Inference Model/Trained Model |
Note: The above accuracy metrics are mAP(0.5:0.95) on the CrowdHuman validation set.
Text Detection Module¶
| Model Name | Detection Hmean (%) | Model Size (M) | Model Download Link |
|---|---|---|---|
| PP-OCRv4_mobile_det | 77.79 | 4.2 M | Inference Model/Trained Model |
| PP-OCRv4_server_det | 82.69 | 100.1 M | Inference Model/Trained Model |
Note: The above accuracy metrics are evaluated on PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, and handwritten scenarios, with 500 images for detection.
Text Recognition Module¶
| Model Name | Recognition Avg Accuracy (%) | Model Size (M) | Model Download Link |
|---|---|---|---|
| PP-OCRv4_mobile_rec | 78.20 | 10.6 M | Inference Model/Trained Model |
| PP-OCRv4_server_rec | 79.20 | 71.2 M | Inference Model/Trained Model |
Note: The above accuracy metrics are evaluated on PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, and handwritten scenarios, with 11,000 images for text recognition.